This invention relates to imaging devices and more particularly to imaging devices that automatically implement statistical quality control.
Image output devices (IOTs) have known limitations on their capability to generate images. While image defects may exist in every image generated by an IOT, so long as the defects are such that they are within the limitations on the capability of the IOT, no service or correction is required. However, when the defects in the image generated by the IOT are such that the IOT has the capability to generate higher quality images, corrective measures should be taken. In order to determine whether the images generated by an IOT are within the tolerances of the output of the IOT, it is common practice to occasionally print test pattern pages. Commercial print operations often use statistical quality control (SQC) of their printed products to be able to guarantee to their customers the quality of the prints. These test pattern pages are analyzed either by operators or service technicians or automatically using image processing algorithms to generate image quality data. This image quality data is compared to the tolerances for the output of the IOT to determine whether service or other corrective measures are required. It is preferable that corrective measures be taken as soon as the image quality falls outside the tolerances that the IOT can generally be expected to meet.
The process of making measurements from the test pattern images is to a certain extent subjective and therefore can be prone to generation of erroneous image data. It is undesirable to request a service call or take other corrective measures based upon defective image quality data. A common practice for detecting banding and streaking defects is to compute the 1D amplitude spectrum of test prints, analyze them, and then conclude whether banding or streaking is a problem and the characteristics of the problem. Prior art practices were to perform the image quality analysis based on a test print, or multiple test prints to determine the current state of the image quality of the IOT. Some systems including IOTs have the capability of storing image quality data in a database relating the image quality data to the time at which it was analyzed. One such system is the six sigma image quality system. With the advent of six-sigma image quality (SSIQ) system and other similar systems in which image quality data is stored in a time stamped manner, it becomes possible to track the image quality performance over time using the image quality database, IQDB. Such tracking can even be automated.
One type of prior art SSIQ system is shown in FIG. 10. Such prior art SSIQ systems 100 include a printer system 102 having a controller (DFE) 104 and an IOT 106. For submission of regular print jobs (“customer jobs”), digital data are sent from a regular print job submission generator 108 to the printer system 102. In FIG. 10, the printer system 102 is assumed to be a printer, however, it is within the scope of the disclosure for the printer system 102 to be a copier. Regular print jobs result in hardcopy output 110 for the customer.
A module 112 is responsible for generating the data resulting in hardcopy test patterns which can be analyzed for SQC purposes. Conceptually, this function is different from traditional controller 104 functionality, but with certain SSIQ physical architectures, this module 112 may be integrated with the controller 104. When the printer system 102 is a copier, this module 112 is the scanning system that generates data for generating a copy based on a test pattern being disposed on the scanner bed. The basic function of this module 112 is to submit the desired test pattern for printing on the desired IOT 106, specifying the desired substrate and other conditions, at the desired time. To assess the banding defects of an IOT 106, a common practice is to print appropriate test patterns such as uniform full-page halftone images, and then measure the print quality by extracting a 1D profile.
An example of such a pattern is a uniform grey image at 50% CMY. Another example is a test pattern with three uniform segments, white, 40% grey, and 70% grey. Depending on the set of failures the system is designed to diagnose, the customer will be directed to print one or more copies of the test pattern if the IOT 106 is a printer or make one or more copies of the test pattern if the IOT 106 is a copier. If the IOT 106 is a copier, the user will also be given additional directions regarding where the original test pattern needs to be placed, e.g. in the document feeder or on the platen glass, and whether to enlarge/reduce the image when making copies.
The SSIQ system 100 may also collect all the meta-data corresponding to the test pattern that is about to be submitted for printing. The meta-data covers all data that is necessary for the subsequent print quality analysis, as well as all the available data that is deemed to be relevant for the subsequent SQC analysis. Such data may include IOT identification, time, date, position relative to print jobs, IOT machine parameters, environmental parameters (temperature, humidity), substrate type, halftone type and other imaging parameters and instructions for the Print Quality Analysis system 114 regarding test pattern content and desired analysis.
The SSIQ may also create an identification (PSN), which is unique for the page that will be printed (e.g., a print serial number) and store the meta-data linked with the PSN, in a manner such that it can be retrieved later on by both the Print Quality Analysis system 114 and for SQC diagnostic engine 116. For example, the PSN and corresponding meta-data can be stored in a record in a database 118. The test pattern submitted by the test sample generation module 112 results in a single page hardcopy output 120 containing the PSN in some form, e.g. as barcode. Corresponding to the hardcopy output page 120, meta-data and PSN has been stored, for example in a database 118.
The hardcopies of the test samples 120 are moved by an operator from the printer output tray to the Print Quality Analysis system 114. Typically, once the copies of the test pattern are made if the IOT is a copier or test patterns are printed if the IOT is a printer, the operator scans the test patterns 120 either using an external scanner or the scanner on their copier. If the set of failures does not include any defects related to the scanner system of a copier, then test patterns stored internally in the machine or on a server may be printed, as opposed to scanning hard copy test patterns and the scanner on the copier may be used for scanning the printed test images instead of an external scanner.
As mentioned above, typically, the Print Quality Analysis system 114 involves a flatbed scanner with automatic document feeder (ADF). The Print Quality Analysis system 114 may be responsible for automatically decoding the PSN if one is present and retrieving information based on the PSN from the database 118. The Print Quality Analysis system 114 can retrieve information that is necessary to perform an accurate analysis, for example substrate information. Optionally, it can retrieve information about the test pattern content and which specific analyses should be performed.
The scanned images are then analyzed by the Print Quality Analysis system 114 to characterize the banding or streaking defects in terms of quantitative parameters by evaluation of the banding metric or streaking metric. Detection of banding and streaking defects is a similar process, therefore only banding detection will be described with the understanding that streaking detection is similar. Once the test images are scanned, the (one-dimensional) L* variations across the image are obtained from the scanned data. Spectral analysis is the performed on the 1D profile by applying Fourier analysis plus some additional signal processing on it to generate banding defects data. As the first step, the Fourier transform of the L* data, FFT(L*) is obtained. Next, the frequency domain data is modulated via a visual transformation function that applies the blurring and edge enhancement of the human visual system. The visually transformed data is then converted back to space domain data, denoted here, as Lf*. Finally, a visual banding metric number, denoted here, as VBL number, is evaluated by first, computing the deviation from average of the Lf* data, and next, by computing a running average of the squared data, and finally, by obtaining the square root of the running average. The image quality outputs from this analysis consist of two types of data, a raw array data of the 1D L*(a*/b*) amplitude spectrum as a function of frequency and a summarized banding defect record. The banding defect record contains a list of detectable banding defects with their corresponding characteristics. The banding defect record can always be recomputed from the raw amplitude spectrum. However, the raw amplitude spectrum cannot be recomputed from the banding defect record.
The Print Quality Analysis system 114 can then decode the information and store it for later use by the SQC diagnostic engine 116. Next, key features are extracted from the banding metric outputs. The feature extraction process abstracts the information in the banding metric outputs into a few key qualitative values, such as a banding defect record including amplitude, frequency and spread data.
The measurement results are stored for later use by the SQC diagnostic engine 116, for example by adding the results to the database 118, linked with the given PSN. The diagnostic engine 116 can retrieve and process the measurement results linked with the meta-data via the PSN. Additionally, the controller 104 can retrieve the results and provide direct feedback to the operator. The sample meta-data can be encoded directly on the sample (in addition to or in place of the PSN).
The few key qualitative values extracted from the banding metric outputs, e.g. a banding defect record including amplitude, frequency and spread data, are passed from the database 118 to the SQC diagnostic engine 116 to determine the print engine failure that caused the banding defect. Once, the failures are identified, the user is provided with the appropriate repair action, which may be to replace a customer replaceable unit, to perform cleaning procedures, or to place service call.
The ability to detect and track the degree of banding defects over time is important for monitoring the print quality of the image output devices (“IOTs”). Maintaining such a record of banding defects provides crucial information about an IOT and facilitates diagnostics on the print engine. Banding defects (i.e. undesired 1D periodic variations) can be characterized by their amplitude (in L* or a* or b* etc) and frequency. Due to the size limitation/variation of the test pattern to be measured (which imposes a limitation on frequency sampling resolution) and the measurement noises in print quality assessment, capturing the variation (spread) of the accuracy of the measured frequency (such as using confident interval) helps to characterize banding defects. Thus, the characteristics of a banding defect are referred to herein as the banding defect's amplitude a, frequency f and frequency-spread σ.
Currently the IQ data generally will be processed instantaneously and used as an indication of the “banding performance” of this given IOT at that moment. However, the accuracy of such instantaneous processing heavily depends on the accuracy of the current IQ measurement and the extent to which the current prints truly reflect the current banding performance of the IOT. Incorporating the past IQ results could alleviate the dependency on the accuracy of the current banding performance measurements and provide better repeatability of the measurements. Incorporating the past IQ results reduces the error in the banding performance measurements caused by measuring some unreasonable bad prints (or good ones) to more accurately reflect the current state of the IOT. Finally, instantaneous processing of current IQ data only precludes the possibility of inferring the future state of the banding performance of the IOT.
The errors arising from the inaccuracy of a single current IQ measurement or a current print that does not truly reflect the current banding performance of the IOT could be reduced by taking more measurements (analyze more prints) at the time of interest. However, analyzing more prints at the time of interest will not change the fact that you can do a better job if you store the IQ data over time and make intelligent use of it. Additionally, it is obvious that you cannot predict the trend if you take measurement at a fixed point in time and throw it away afterward. The ability to predict future IQ data is very useful for preventing issues caused by long-term performance drifting.
As previously mentioned, SSIQ systems generate the image quality data of the IOTs and store the image quality data into a database (image quality database (“IQDB”). In many current SSIQ systems, banding defect records and/or the raw amplitude spectrum and its corresponding frequency resolution are stored. Storing all of this data can become inefficient. Using a letter-size print via short edge feed as an example, the amplitude spectrum has 6600 (11 in ×600 DPI) values at 6600 corresponding frequencies (f=i·Δf where i=0-6599). In order to capture full spectral data so that you can recover the corresponding banding defect record from it, you need to store 6601 values (6600 amplitudes and one the frequency resolution Δf) per print in the IQDB. If you store the banding record instead, it requires only 3 values (a, f, σ) per detected banding defect. Depending on how many banding defects that you detected on the given print, the storage required for keeping the record varies. But the number of banding defects detected per print are generally much smaller than 2200 (6600/3). Hence from efficiency point of view, storing banding records in IQDB is certainly much better.
The benefits of storing only defect records are that less storage space is required and there is no need to re-compute the banding record from raw amplitude spectrum. The disadvantage is that the full amplitude spectrum cannot be recovered from its banding record. A challenge is presented when banding records available in the IQDB are combined for assessing/predicting the banding performance of the IOTs later.
The above referenced challenge can be illustrated with reference to two existing banding records in IQDB having the following values: Record #1: one banding defect with (a, f, σ)=(1.0, 1.0, 0.5) and Record #2: one banding defect with (a, f, σ)=(1.0, 1.1, 0.5). The two records may be processed by treating bands with f=1.0 in #1 and f=1.1 in #2 as two different bands. Alternatively, the two records may be processed by taking the average of these records across time and concluding that there are two banding defects presented, one with (a, f, σ)=(0.5, 1.0, 0.5), 0.5 in amplitude due to the averaging across time, and the other with (a, f, σ)=(0.5, 1.1, 0.5). As an additional alternative, the two records may be processed by setting a tolerance (say 0.1) on the measured frequency so that the bands in both records are considered a single band, then inferring that the frequency should be roughly equal to the average of the two records in frequency so that these two records represent a banding defect with (a, f, σ)=(1.0, 1.05, 0.5). Here the amplitude remains a=1.0 after the averaging, frequency changes to f=1.05 which is the average of the 2 records, and the frequency-spread is assumed unchanged. As the above example illustrates, without a well-thought strategy, combining results across banding records could do harms rather than good. If on the other hand the full spectral data were available in IQDB, combining the results would have been more straightforward.
Because systems including IOTs do not include unlimited memory, it would be preferable to store image quality data efficiently. In the case of banding defects, for analysis and prediction purposes it is more important to capture all the detectable bandings over time rather than the entire 1D amplitude spectrum. Obviously, if the IQDB stores all the array data of full spectrum over time, the detectable banding defects can be re-computed when needed. This is certainly not efficient since the amount of data required to capture all detectable banding defects are much smaller than that of the raw spectral data.
Even if the storage is not an issue, there is still the issue of how to combine the spectral data over time. If the IQDB stores only the summarized banding defect records for efficiency and storage reasons, combining these records over time becomes even more of a challenge than that described above with regard to Record #1 and Record #2. A method of combining such records over time would be appreciated.
The disclosed device and method efficiently stores image quality data over time in a manner facilitating the utilization of the data to schedule servicing and/or to confirm the accuracy of the most recently acquired image quality data. An image quality defect detection algorithm, which utilizes the historical data from image quality database (IQDB) about the IOTs is disclosed that permits predictions to made about future image quality. The disclosed device and method are particularly useful for customers who track the image quality of their IOTs via database such as utilizing a six-sigma image quality system. This algorithm first reconstructs isolated amplitude spectrum from each image quality defect record in IQDB. It then combines these spectra into one cumulated spectrum via probability summation. After that, a prediction term is added to this cumulated spectrum to account for the missing data between now and the last available data in the IQDB. The disclosed device and method keep the advantages of storing only the defect records in IQDB. The disclosed device and method combine image quality defect records by applying probability summation.
The disclosed algorithm advantageously provides prediction capability and improves measurement robustness by incorporating the outcome from historical data. The disclosure also provides an efficient implementation of the algorithm by utilizing a recursive function. This algorithm also provides the flexibility for users to investigate short-term and long-term IQ trend with a simple change of probability summation parameter and/or prediction scaling factor.
According to one aspect of the disclosure, a method of detecting banding defects in the output of an image output device comprising the steps of generating a test pattern, analyzing the generated test pattern, storing spectral banding defect records, repeating the generating, analyzing and storing steps, reconstructing an isolated spectrum, constructing a cumulative spectrum and analyzing the cumulative spectrum. The generating a test pattern step is performed at a known time. The analyzing the generated test pattern step includes generating spectral banding defect records including the one dimensional amplitude, frequency and spread of each detected banding defect in the test pattern. The spectral banding defect records are stored in memory linked to a time stamp indicating the known time when the test pattern was generated. The repeating the generating, analyzing and storing steps is performed at a plurality of different known times to generate a database of time stamped spectral banding defect records. The reconstructing an isolated spectrum step is performed on each spectral banding defect record in the database. The constructing a cumulative spectrum step is performed by probabilistic summation of the isolated spectra. The analyzing the cumulative spectrum step utilizes an image quality engine.
According to another aspect of the disclosure, a method of detecting image quality defects in the output of an image output device includes the steps of generating a test pattern at a known time, analyzing the test pattern to generate image quality defect records, storing the image quality defect records in memory linked to a time stamp indicating known time when the test pattern was generated, repeating the generating, analyzing and storing steps at a plurality of different known times to generate a database of time stamped image quality defect records, reconstructing isolated defect data from each of a plurality of image quality defect records in the database, constructing cumulative defect data by probabilistic summation of the isolated defect data and analyzing the cumulative defect data using an image quality assessment/analysis engine.
Additional features and advantages of the present invention will become apparent to those skilled in the art upon consideration of the following detailed description of preferred embodiments exemplifying the best mode of carrying out the invention as presently perceived.
Corresponding reference characters indicate corresponding parts throughout the several views. Like reference characters tend to indicate like parts throughout the several views.